We first verify that the semantic distances over these semantic spaces properly anti-correlate to human similarity ratings: words that humans judge to be highly similar in meaning should not be far from one another in the embedding spaces. We therefore assess the four sets of word embeddings on a word semantic similarity benchmark, the men dataset [1]}; for all models, we test the Euclidean distance and the cosine distance as a distance over vectors. Results in Figure REF  highlight that while all semantic metrics properly anti-correlate,
cosine distance yields higher correlations with human similarity judgments than Euclidean distance, for all embedding spaces.
